A Generalized Spatial Fuzzy C-Means Algorithm for Medical Image Segmentation

Huynh Van Luong, Jong-Myon Kim

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Medical image segmentation is an indispensable process in viewing and measuring various structures in the brain. However, medical images are inherently low contrast, vague boundaries, and high correlative. The traditional fuzzy c-means (FCM) clustering algorithm considers only the pixel attributes. This leads to accuracy degradation with image segmentation. To solve this problem, this paper proposes a robust segmentation technique, called a Generalized Spatial Fuzzy C-Means (GSFCM) algorithm, that utilizes both given pixel attributes and the spatial local information which is weighted correspondingly to neighbor elements based on their distance attributes. This improves the segmentation performance dramatically. Experimental results with several magnetic resonance (MR) images show that the proposed GSFCM algorithm outperforms the traditional FCM algorithms in the various cluster validity functions.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems, 2009 : FUZZ-IEEE 2009
Publication date2009
ISBN (Print)978-1-4244-3596-8
ISBN (Electronic)978-1-4244-3597-5
Publication statusPublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) - Jeju Island, Korea, Republic of
Duration: 20 Aug 200924 Aug 2009


Conference2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Country/TerritoryKorea, Republic of
CityJeju Island
Internet address


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